Model parameter estimation and analysis: Understanding parametric structure

Hsuehmin Li, Karen Watanabe, David Auslander, Robert C. Spear

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.

Original languageEnglish (US)
Pages (from-to)97-111
Number of pages15
JournalAnnals of Biomedical Engineering
Volume22
Issue number1
DOIs
StatePublished - Jan 1 1994
Externally publishedYes

Keywords

  • Benzene
  • Monte Carlo simulations
  • Parameter estimation
  • Parametric analysis
  • Pharmacokinetic models

ASJC Scopus subject areas

  • Biomedical Engineering

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